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Atmospheric characterization of temperate rocky planets through reflection spectroscopy

Presentation #102.153 in the session Poster Session.

Published onJun 20, 2022
Atmospheric characterization of temperate rocky planets through reflection spectroscopy

The high-contrast imaging technique is poised to provide insights into those planets orbiting several astronomical units from their host star so that their equilibrium temperature is low enough to let different chemical and dynamical behavior emerge (e.g., condensation mechanism, cold trap effects, etc.) with respect to their better-studied hot counterparts. This technique has been successfully tested in studies of star- and planet-forming regions. Future direct-imaging exoplanet space mission and mission concepts, e.g., Nancy Grace Roman Space Telescope (Roman), Habitable Exoplanet Imaging Mission (HabEx), Large Ultra- Violet/Optical/ InfraRed Surveyor (LUVOIR), and Starshade rendezvous probe, will have the opportunity to observe the starlight reSected by exoplanets via high-contrast imaging and also to unveil their atmospheric structure. For example, clouds, if present in the atmosphere, are the primary factor that controls the appearance of an exoplanet.

Given the importance of the topic and the scarcity of studies, we use our Bayesian retrieval method (ExoReL-R, Damiano & Hu 2020; Damiano et al. 2020; Damiano & Hu 2021) to explore the possible atmospheric constraints from reflected light spectra of temperate rocky exoplanets.

The rocky planets in the Solar System indicate that CO2-dominated atmospheres are likely. An important objective of this study is to determine whether reflected light spectroscopy can distinguish types of rocky planets (e.g., Earth-like versus Venus-like) and characterize their atmospheric composition. We thus study the ability of spectral retrieval on atmospheric scenarios that correspond to not only modern Earth, but also Archean Earth and planets with CO2-dominated atmospheres (resembling Venus and Mars). We found that, with enough signal-to-noise and spectral resolution, our model is capable to distinguish different atmospheric scenarios without any a priori knowledge about the dominant gas.

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